The respiratory and heart signals and their corresponding rates are fundamental vital signs. The respiratory rate is one of the most important vital signs for patient monitors in the general ward. Usually, the respiratory and heart signals are detected through sensor electrodes attached to the person. Such signals can be generated using an obtrusive approach, which utilizes the attachment of cables, as applied to patients who are on the general ward for a significant amount of time. Alternatively, also unobtrusive measurement of respiratory and heart signals for a patient monitoring can be carried out, e.g. by using a battery-powered tri-axial accelerometer attached to a body part of the patient. In this way, seismocardiogram signals can be detected and used to determine the heart-rate and respiration-rate.
Also vibrations caused by the mechanical activity of the heart can be measured by using the ballistocardiography (BCG) technique, where the blood transport causes small changes in center-of-gravity of the person, which can be measured by measuring the small displacements of a spring-mounted bed. Alternatively, vibrations of the heart or blood-transport can be measured directly on the skin of a person via an accelerometer. The afore-mentioned techniques are known as seismocardiography (SCG). Further ways of measuring displacements on the body include kinetocardiography (KCG) and phonocardiography (using microphones in a cavity placed on the skin). It is noted that kinetocardiography and phonocardiography also relate to the measurement of the low frequencies of the anterior chest wall, similar to what is measured by using an accelerometer.
The SCG signals can be analyzed in order to detect respiratory and heart signals. For instance, two important events in a single cardiac cycle can be observed, from which one relates to aortic valve opening (AO) and the other event relates to aortic valve closing (AC). It is important to distinguish between these two events so that the vital signs are detected accurately and reliably from the SCG signals. However, processing devices and systems for SCG-signal processing known in the art are not able to output the heart-rate properly, especially when irregular heart-rates occur, for instance due to heart diseases or movement artifacts.
Pandia et al. “Motion artifact cancellation to obtain heart sounds from a single chest-worn accelerometer”, 2010 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), discloses a method of extracting primary heart sound signals from chest-worn accelerometer data in the presence of motion artifacts, wherein the proposed method outperforms noise removal techniques such as wavelet denoising and adaptive filtering.
Pandia et al. “Extracting respiratory information from seismocardiogram signals acquired on the chest using a miniature accelerometer,” Physiol. Meas. vol. 33, pp. 1643-1660, 2012 discloses a method for extracting respiration signals derived from cardiac information.
US 2010/discloses a heart sound analyzer which receives electrical signal generated by a heart sound sensor, wherein the heart sound analyzer comprises an envelope extractor which processes the received signal to extract an envelope, wherein the heart sound analyzer further comprises a heart sound detector which utilizes an algorithm to detect heart sound within the envelope signal